Mr. Zhenyu Liu | Simulation Algorithms | Best Researcher Award

Mr. Zhenyu Liu | Simulation Algorithms | Best Researcher Award

Inner Mongolia Agricultural University | China

Zhenyu Liu is a master’s degree candidate at the College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, specializing in agricultural engineering and information technology with a strong research focus on smart agricultural equipment and simulation-based optimization. His scholarly contributions include peer-reviewed publications indexed in SCI and Scopus, accompanied by documented citations and available research records. His Scopus profile reflects two indexed documents with an h-index of one, while Google Scholar also lists his publications and citations, verifying his emerging academic presence. His research documents, citation metrics, and publication outputs collectively highlight his early but impactful scientific development.

Publication Profile

Scopus

Education Background

Zhenyu Liu is currently pursuing a master’s degree at the College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, where he has developed a strong foundation in agricultural engineering, information technology, and intelligent equipment design. His coursework emphasizes agricultural machinery systems, discrete element simulations, and engineering data analysis. His academic journey is supported by participation in nationally guided scientific development projects, where he has gained experience in research documentation, simulation modeling, and practical field experimentation. His education further includes training in advanced software tools and exposure to interdisciplinary agricultural technologies that strengthen his capability for independent research and scientific publication.

Professional Experience

Zhenyu Liu has developed his professional experience through active involvement in research tasks within a Central Government Guided Local Science and Technology Development Fund Project, where he contributed to simulation design, model calibration, and mechanical interface analysis. His experience includes operation of agricultural machinery, execution of engineering experiments, and preparation of research documents for scientific dissemination. He has collaborated with faculty teams in analyzing agricultural material behavior using EDEM, preparing manuscripts for SCI journals, and documenting experimental outcomes. His professional exposure also extends to patent development, agricultural equipment evaluation, and contributing to collaborative academic outputs verified through Scopus and Google Scholar records.

Awards and Honors

Zhenyu Liu’s achievements include securing three authorized Chinese utility model patents related to agricultural machinery design and optimization, reflecting his commitment to research innovation. His publications in SCI-indexed journals demonstrate recognized scientific contribution, supported by citation records that validate his research impact. His documented output in Scopus and Google Scholar enhances his academic credibility, while participation in government-funded scientific research adds value to his professional development. Although early in his career, these accomplishments serve as evidence of his dedication to advancing agricultural engineering knowledge and earning academic recognition through verifiable documents, indexed publications, and measurable citation performance.

Research Focus

Zhenyu Liu’s research centers on smart agricultural machinery, discrete element simulation, agricultural material behavior modeling, and engineering optimization for crop mechanization. His work emphasizes calibration of simulation parameters, analysis of seed-material interactions, and evaluation of agricultural equipment performance through experimental validation. With publications indexed in SCI and Scopus, his research outputs are supported by documented citations and scholarly visibility across recognized databases. His focus also includes improving agricultural production efficiency using computational tools, advancing interface modeling of agricultural materials, and contributing verified scientific findings through peer-reviewed articles and research documents that showcase his growing academic and technical expertise.

Publications

Liu, Z., Yan, J., Liu, F., & Wang, L. (2025). Calibration and testing of discrete element simulation parameters for the presoaked Cyperus esculentus L. rubber interface using EDEM. Agronomy. Cited by documented sources in Scopus.

Yan, J., Liu, Z., & Liu, F. (2025). Calibration and analysis of seeding parameters of soaked Cyperus esculentus L. seeds. Applied Sciences. Cited by one Scopus-indexed article.

Shu Tang | Multi-view 3D reconstruction | Best Researcher Award

Assoc Prof Dr. Shu Tang | Multi-view 3D reconstruction | Best Researcher Award

Associate Professor, Chongqing University of Posts and Telecommunications, China

Tang Shu is an Associate Professor at the College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China. Specializing in computer vision, his research focuses on image restoration and 3D reconstruction.

Profile

ORCID

🎓 Education:

Tang Shu received his M.E. degree from Chongqing University of Posts and Telecommunications in 2007 and his Ph.D. from Chongqing University in 2013.

💼 Experience:

Tang Shu has been an Associate Professor at Chongqing University of Posts and Telecommunications since February 24, 2014, accumulating over 10 years of professional experience.

🔬 Research Interests:

Tang Shu’s research encompasses computer vision, image restoration, and 3D reconstruction. He has developed strategies like EFERS, SI-SDF, and VMES for enhancing surface information reconstruction, geometry optimization, and material estimation.

🏆 Awards:

Tang Shu has published over 10 academic papers, presided over 10 scientific research projects, and been granted 6 invention patents. He has completed significant projects including the 61601070 project and ongoing ones like CSTB2023NSCQ-MSX0680.

Publications

Here are some of Tang Shu’s notable publications:

Deep Convolutional-Neural-Network-Based Channel Attention for Single Image Dynamic Scene Blind Deblurring – Computer Aided Geometric Design, 2020.

Multi-Regularization-Constrained Blur Kernel Estimation Method for Blind Motion Deblurring – IEEE Transactions on Circuits and Systems for Video Technology, 2020.

Non-blind Image Deblurring Method by Local and Nonlocal Total Variation Models – Signal Processing, 2019.

Joint Texture/Depth Power Allocation for 3-D Video SoftCast – IEEE Transactions on Multimedia, 2018.

REAL-TIME TRACKING OF VEHICLES WITH SIAMESE NETWORK AND BACKWARD PREDICTION – IEEE Access, 2021.